Overview

Dataset statistics

Number of variables17
Number of observations8693
Missing cells597
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory122.0 B

Variable types

Text1
Categorical5
Boolean3
Numeric8

Alerts

CryoSleep is highly overall correlated with FoodCourt and 4 other fieldsHigh correlation
Deck is highly overall correlated with HomePlanetHigh correlation
FoodCourt is highly overall correlated with CryoSleepHigh correlation
HomePlanet is highly overall correlated with DeckHigh correlation
RoomService is highly overall correlated with CryoSleepHigh correlation
ShoppingMall is highly overall correlated with CryoSleepHigh correlation
Spa is highly overall correlated with CryoSleepHigh correlation
VRDeck is highly overall correlated with CryoSleepHigh correlation
VIP is highly imbalanced (84.3%)Imbalance
ID_No is highly imbalanced (54.3%)Imbalance
Deck has 199 (2.3%) missing valuesMissing
Cabin_No has 199 (2.3%) missing valuesMissing
Cabin_Side has 199 (2.3%) missing valuesMissing
PassengerId has unique valuesUnique
Age has 178 (2.0%) zerosZeros
RoomService has 117 (1.3%) zerosZeros
FoodCourt has 116 (1.3%) zerosZeros
ShoppingMall has 153 (1.8%) zerosZeros
Spa has 146 (1.7%) zerosZeros
VRDeck has 139 (1.6%) zerosZeros

Reproduction

Analysis started2024-12-19 05:33:08.321758
Analysis finished2024-12-19 05:39:15.500110
Duration6 minutes and 7.18 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

PassengerId
Text

UNIQUE 

Distinct8693
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size68.0 KiB
2024-12-19T00:39:15.649677image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters60851
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8693 ?
Unique (%)100.0%

Sample

1st row0001_01
2nd row0002_01
3rd row0003_01
4th row0003_02
5th row0004_01
ValueCountFrequency (%)
0001_01 1
 
< 0.1%
0031_01 1
 
< 0.1%
0003_01 1
 
< 0.1%
0003_02 1
 
< 0.1%
0004_01 1
 
< 0.1%
0005_01 1
 
< 0.1%
0006_01 1
 
< 0.1%
0006_02 1
 
< 0.1%
0007_01 1
 
< 0.1%
0008_01 1
 
< 0.1%
Other values (8683) 8683
99.9%
2024-12-19T00:39:15.935537image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 12459
20.5%
1 9827
16.1%
_ 8693
14.3%
2 5017
8.2%
3 4039
 
6.6%
4 3790
 
6.2%
6 3664
 
6.0%
5 3606
 
5.9%
8 3557
 
5.8%
7 3410
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 52158
85.7%
Connector Punctuation 8693
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12459
23.9%
1 9827
18.8%
2 5017
9.6%
3 4039
 
7.7%
4 3790
 
7.3%
6 3664
 
7.0%
5 3606
 
6.9%
8 3557
 
6.8%
7 3410
 
6.5%
9 2789
 
5.3%
Connector Punctuation
ValueCountFrequency (%)
_ 8693
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60851
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12459
20.5%
1 9827
16.1%
_ 8693
14.3%
2 5017
8.2%
3 4039
 
6.6%
4 3790
 
6.2%
6 3664
 
6.0%
5 3606
 
5.9%
8 3557
 
5.8%
7 3410
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60851
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12459
20.5%
1 9827
16.1%
_ 8693
14.3%
2 5017
8.2%
3 4039
 
6.6%
4 3790
 
6.2%
6 3664
 
6.0%
5 3606
 
5.9%
8 3557
 
5.8%
7 3410
 
5.6%

HomePlanet
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.0 KiB
Earth
4803 
Europa
2131 
Mars
1759 

Length

Max length6
Median length5
Mean length5.0427931
Min length4

Characters and Unicode

Total characters43837
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEuropa
2nd rowEarth
3rd rowEuropa
4th rowEuropa
5th rowEarth

Common Values

ValueCountFrequency (%)
Earth 4803
55.3%
Europa 2131
24.5%
Mars 1759
 
20.2%

Length

2024-12-19T00:39:16.057600image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T00:39:16.146474image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
earth 4803
55.3%
europa 2131
24.5%
mars 1759
 
20.2%

Most occurring characters

ValueCountFrequency (%)
a 8693
19.8%
r 8693
19.8%
E 6934
15.8%
t 4803
11.0%
h 4803
11.0%
u 2131
 
4.9%
o 2131
 
4.9%
p 2131
 
4.9%
M 1759
 
4.0%
s 1759
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35144
80.2%
Uppercase Letter 8693
 
19.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 8693
24.7%
r 8693
24.7%
t 4803
13.7%
h 4803
13.7%
u 2131
 
6.1%
o 2131
 
6.1%
p 2131
 
6.1%
s 1759
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
E 6934
79.8%
M 1759
 
20.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 43837
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 8693
19.8%
r 8693
19.8%
E 6934
15.8%
t 4803
11.0%
h 4803
11.0%
u 2131
 
4.9%
o 2131
 
4.9%
p 2131
 
4.9%
M 1759
 
4.0%
s 1759
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43837
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 8693
19.8%
r 8693
19.8%
E 6934
15.8%
t 4803
11.0%
h 4803
11.0%
u 2131
 
4.9%
o 2131
 
4.9%
p 2131
 
4.9%
M 1759
 
4.0%
s 1759
 
4.0%

CryoSleep
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.6 KiB
False
5558 
True
3135 
ValueCountFrequency (%)
False 5558
63.9%
True 3135
36.1%
2024-12-19T00:39:16.234396image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Destination
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.0 KiB
TRAPPIST-1e
6097 
55 Cancri e
1800 
PSO J318.5-22
796 

Length

Max length13
Median length11
Mean length11.183136
Min length11

Characters and Unicode

Total characters97215
Distinct characters23
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTRAPPIST-1e
2nd rowTRAPPIST-1e
3rd rowTRAPPIST-1e
4th rowTRAPPIST-1e
5th rowTRAPPIST-1e

Common Values

ValueCountFrequency (%)
TRAPPIST-1e 6097
70.1%
55 Cancri e 1800
 
20.7%
PSO J318.5-22 796
 
9.2%

Length

2024-12-19T00:39:16.333846image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T00:39:16.425560image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
trappist-1e 6097
46.6%
55 1800
 
13.8%
cancri 1800
 
13.8%
e 1800
 
13.8%
pso 796
 
6.1%
j318.5-22 796
 
6.1%

Most occurring characters

ValueCountFrequency (%)
P 12990
13.4%
T 12194
12.5%
e 7897
 
8.1%
S 6893
 
7.1%
- 6893
 
7.1%
1 6893
 
7.1%
A 6097
 
6.3%
I 6097
 
6.3%
R 6097
 
6.3%
5 4396
 
4.5%
Other values (13) 20768
21.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 53760
55.3%
Lowercase Letter 16897
 
17.4%
Decimal Number 14473
 
14.9%
Dash Punctuation 6893
 
7.1%
Space Separator 4396
 
4.5%
Other Punctuation 796
 
0.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 12990
24.2%
T 12194
22.7%
S 6893
12.8%
A 6097
11.3%
I 6097
11.3%
R 6097
11.3%
C 1800
 
3.3%
O 796
 
1.5%
J 796
 
1.5%
Lowercase Letter
ValueCountFrequency (%)
e 7897
46.7%
c 1800
 
10.7%
i 1800
 
10.7%
r 1800
 
10.7%
n 1800
 
10.7%
a 1800
 
10.7%
Decimal Number
ValueCountFrequency (%)
1 6893
47.6%
5 4396
30.4%
2 1592
 
11.0%
3 796
 
5.5%
8 796
 
5.5%
Dash Punctuation
ValueCountFrequency (%)
- 6893
100.0%
Space Separator
ValueCountFrequency (%)
4396
100.0%
Other Punctuation
ValueCountFrequency (%)
. 796
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 70657
72.7%
Common 26558
 
27.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 12990
18.4%
T 12194
17.3%
e 7897
11.2%
S 6893
9.8%
A 6097
8.6%
I 6097
8.6%
R 6097
8.6%
c 1800
 
2.5%
i 1800
 
2.5%
r 1800
 
2.5%
Other values (5) 6992
9.9%
Common
ValueCountFrequency (%)
- 6893
26.0%
1 6893
26.0%
5 4396
16.6%
4396
16.6%
2 1592
 
6.0%
3 796
 
3.0%
8 796
 
3.0%
. 796
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 97215
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 12990
13.4%
T 12194
12.5%
e 7897
 
8.1%
S 6893
 
7.1%
- 6893
 
7.1%
1 6893
 
7.1%
A 6097
 
6.3%
I 6097
 
6.3%
R 6097
 
6.3%
5 4396
 
4.5%
Other values (13) 20768
21.4%

Age
Real number (ℝ)

ZEROS 

Distinct80
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.790291
Minimum0
Maximum79
Zeros178
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size68.0 KiB
2024-12-19T00:39:16.528710image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q120
median27
Q337
95-th percentile55
Maximum79
Range79
Interquartile range (IQR)17

Descriptive statistics

Standard deviation14.341404
Coefficient of variation (CV)0.49813335
Kurtosis0.1695703
Mean28.790291
Median Absolute Deviation (MAD)9
Skewness0.43110236
Sum250274
Variance205.67587
MonotonicityNot monotonic
2024-12-19T00:39:16.648591image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 438
 
5.0%
24 324
 
3.7%
18 320
 
3.7%
21 311
 
3.6%
19 293
 
3.4%
23 292
 
3.4%
22 291
 
3.3%
20 277
 
3.2%
26 268
 
3.1%
28 267
 
3.1%
Other values (70) 5612
64.6%
ValueCountFrequency (%)
0 178
2.0%
1 67
 
0.8%
2 75
0.9%
3 75
0.9%
4 71
 
0.8%
5 33
 
0.4%
6 40
 
0.5%
7 52
 
0.6%
8 46
 
0.5%
9 42
 
0.5%
ValueCountFrequency (%)
79 3
 
< 0.1%
78 3
 
< 0.1%
77 2
 
< 0.1%
76 2
 
< 0.1%
75 4
< 0.1%
74 5
0.1%
73 7
0.1%
72 4
< 0.1%
71 7
0.1%
70 9
0.1%

VIP
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.6 KiB
False
8494 
True
 
199
ValueCountFrequency (%)
False 8494
97.7%
True 199
 
2.3%
2024-12-19T00:39:16.749377image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

RoomService
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1273
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1890268
Minimum-2.3025851
Maximum9.5699011
Zeros117
Zeros (%)1.3%
Negative5758
Negative (%)66.2%
Memory size68.0 KiB
2024-12-19T00:39:16.844625image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-2.3025851
5-th percentile-2.3025851
Q1-2.3025851
median-2.3025851
Q33.7135721
95-th percentile7.1363238
Maximum9.5699011
Range11.872486
Interquartile range (IQR)6.0161572

Descriptive statistics

Standard deviation3.719179
Coefficient of variation (CV)19.675406
Kurtosis-0.78721207
Mean0.1890268
Median Absolute Deviation (MAD)0
Skewness0.986141
Sum1643.2099
Variance13.832292
MonotonicityNot monotonic
2024-12-19T00:39:16.961221image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.302585093 5758
66.2%
0 117
 
1.3%
0.6931471806 79
 
0.9%
1.098612289 61
 
0.7%
1.386294361 47
 
0.5%
1.609437912 28
 
0.3%
2.197224577 25
 
0.3%
2.079441542 24
 
0.3%
1.791759469 24
 
0.3%
2.63905733 21
 
0.2%
Other values (1263) 2509
28.9%
ValueCountFrequency (%)
-2.302585093 5758
66.2%
0 117
 
1.3%
0.6931471806 79
 
0.9%
1.098612289 61
 
0.7%
1.386294361 47
 
0.5%
1.609437912 28
 
0.3%
1.791759469 24
 
0.3%
1.945910149 17
 
0.2%
2.079441542 24
 
0.3%
2.197224577 25
 
0.3%
ValueCountFrequency (%)
9.569901148 1
< 0.1%
9.2023082 1
< 0.1%
9.057888249 1
< 0.1%
9.017119634 1
< 0.1%
9.012986392 1
< 0.1%
9.00797936 1
< 0.1%
9.005895898 1
< 0.1%
9.004791129 1
< 0.1%
8.990939807 1
< 0.1%
8.910045761 1
< 0.1%

FoodCourt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1507
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.39184876
Minimum-2.3025851
Maximum10.3027
Zeros116
Zeros (%)1.3%
Negative5639
Negative (%)64.9%
Memory size68.0 KiB
2024-12-19T00:39:17.082477image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-2.3025851
5-th percentile-2.3025851
Q1-2.3025851
median-2.3025851
Q34.1108739
95-th percentile7.889609
Maximum10.3027
Range12.605285
Interquartile range (IQR)6.413459

Descriptive statistics

Standard deviation3.9335989
Coefficient of variation (CV)10.038564
Kurtosis-0.78458719
Mean0.39184876
Median Absolute Deviation (MAD)0
Skewness0.96336075
Sum3406.3413
Variance15.4732
MonotonicityNot monotonic
2024-12-19T00:39:17.199098image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.302585093 5639
64.9%
0 116
 
1.3%
0.6931471806 75
 
0.9%
1.098612289 53
 
0.6%
1.386294361 53
 
0.6%
1.609437912 33
 
0.4%
1.791759469 31
 
0.4%
2.197224577 28
 
0.3%
1.945910149 27
 
0.3%
2.302585093 27
 
0.3%
Other values (1497) 2611
30.0%
ValueCountFrequency (%)
-2.302585093 5639
64.9%
0 116
 
1.3%
0.6931471806 75
 
0.9%
1.098612289 53
 
0.6%
1.386294361 53
 
0.6%
1.609437912 33
 
0.4%
1.791759469 31
 
0.4%
1.945910149 27
 
0.3%
2.079441542 20
 
0.2%
2.197224577 28
 
0.3%
ValueCountFrequency (%)
10.30269982 1
< 0.1%
10.23001767 1
< 0.1%
10.20621832 1
< 0.1%
10.19727594 1
< 0.1%
9.955415645 1
< 0.1%
9.824498456 1
< 0.1%
9.795790977 1
< 0.1%
9.792611856 1
< 0.1%
9.780585185 1
< 0.1%
9.766062877 1
< 0.1%

ShoppingMall
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1115
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.039248695
Minimum-2.3025851
Maximum10.064415
Zeros153
Zeros (%)1.8%
Negative5795
Negative (%)66.7%
Memory size68.0 KiB
2024-12-19T00:39:17.315990image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-2.3025851
5-th percentile-2.3025851
Q1-2.3025851
median-2.3025851
Q33.0910425
95-th percentile6.8160783
Maximum10.064415
Range12.367
Interquartile range (IQR)5.3936275

Descriptive statistics

Standard deviation3.5532454
Coefficient of variation (CV)90.531556
Kurtosis-0.62897046
Mean0.039248695
Median Absolute Deviation (MAD)0
Skewness1.0445836
Sum341.18891
Variance12.625553
MonotonicityNot monotonic
2024-12-19T00:39:17.431388image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.302585093 5795
66.7%
0 153
 
1.8%
0.6931471806 80
 
0.9%
1.098612289 59
 
0.7%
1.386294361 45
 
0.5%
1.609437912 38
 
0.4%
1.945910149 36
 
0.4%
1.791759469 34
 
0.4%
2.564949357 29
 
0.3%
2.197224577 28
 
0.3%
Other values (1105) 2396
27.6%
ValueCountFrequency (%)
-2.302585093 5795
66.7%
0 153
 
1.8%
0.6931471806 80
 
0.9%
1.098612289 59
 
0.7%
1.386294361 45
 
0.5%
1.609437912 38
 
0.4%
1.791759469 34
 
0.4%
1.945910149 36
 
0.4%
2.079441542 28
 
0.3%
2.197224577 28
 
0.3%
ValueCountFrequency (%)
10.06441522 1
< 0.1%
9.413526084 1
< 0.1%
9.278466201 1
< 0.1%
9.251866119 1
< 0.1%
9.111403624 1
< 0.1%
8.963160243 1
< 0.1%
8.879750799 1
< 0.1%
8.874587876 1
< 0.1%
8.868413285 1
< 0.1%
8.825412915 1
< 0.1%

Spa
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1327
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35425578
Minimum-2.3025851
Maximum10.017173
Zeros146
Zeros (%)1.7%
Negative5507
Negative (%)63.3%
Memory size68.0 KiB
2024-12-19T00:39:17.550081image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-2.3025851
5-th percentile-2.3025851
Q1-2.3025851
median-2.3025851
Q33.9702919
95-th percentile7.3621344
Maximum10.017173
Range12.319758
Interquartile range (IQR)6.272877

Descriptive statistics

Standard deviation3.7715121
Coefficient of variation (CV)10.646297
Kurtosis-0.84537957
Mean0.35425578
Median Absolute Deviation (MAD)0
Skewness0.91834064
Sum3079.5455
Variance14.224303
MonotonicityNot monotonic
2024-12-19T00:39:17.666618image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.302585093 5507
63.3%
0 146
 
1.7%
0.6931471806 105
 
1.2%
1.609437912 53
 
0.6%
1.098612289 53
 
0.6%
1.386294361 46
 
0.5%
1.945910149 34
 
0.4%
1.791759469 33
 
0.4%
2.197224577 28
 
0.3%
2.079441542 28
 
0.3%
Other values (1317) 2660
30.6%
ValueCountFrequency (%)
-2.302585093 5507
63.3%
0 146
 
1.7%
0.6931471806 105
 
1.2%
1.098612289 53
 
0.6%
1.386294361 46
 
0.5%
1.609437912 53
 
0.6%
1.791759469 33
 
0.4%
1.945910149 34
 
0.4%
2.079441542 28
 
0.3%
2.197224577 28
 
0.3%
ValueCountFrequency (%)
10.01717332 1
< 0.1%
9.829410349 1
< 0.1%
9.716796463 1
< 0.1%
9.688993982 1
< 0.1%
9.654128354 1
< 0.1%
9.637632201 1
< 0.1%
9.631547587 1
< 0.1%
9.613803477 1
< 0.1%
9.546455402 1
< 0.1%
9.539787994 1
< 0.1%

VRDeck
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1306
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22919154
Minimum-2.3025851
Maximum10.091335
Zeros139
Zeros (%)1.6%
Negative5683
Negative (%)65.4%
Memory size68.0 KiB
2024-12-19T00:39:17.779806image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-2.3025851
5-th percentile-2.3025851
Q1-2.3025851
median-2.3025851
Q33.6888795
95-th percentile7.299932
Maximum10.091335
Range12.393921
Interquartile range (IQR)5.9914645

Descriptive statistics

Standard deviation3.7409087
Coefficient of variation (CV)16.322193
Kurtosis-0.70293461
Mean0.22919154
Median Absolute Deviation (MAD)0
Skewness0.9962281
Sum1992.3621
Variance13.994398
MonotonicityNot monotonic
2024-12-19T00:39:17.900836image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.302585093 5683
65.4%
0 139
 
1.6%
0.6931471806 70
 
0.8%
1.098612289 56
 
0.6%
1.609437912 51
 
0.6%
1.386294361 47
 
0.5%
1.791759469 32
 
0.4%
2.079441542 30
 
0.3%
1.945910149 29
 
0.3%
2.197224577 25
 
0.3%
Other values (1296) 2531
29.1%
ValueCountFrequency (%)
-2.302585093 5683
65.4%
0 139
 
1.6%
0.6931471806 70
 
0.8%
1.098612289 56
 
0.6%
1.386294361 47
 
0.5%
1.609437912 51
 
0.6%
1.791759469 32
 
0.4%
1.945910149 29
 
0.3%
2.079441542 30
 
0.3%
2.197224577 25
 
0.3%
ValueCountFrequency (%)
10.09133548 1
< 0.1%
9.920147993 1
< 0.1%
9.758808541 1
< 0.1%
9.745312118 1
< 0.1%
9.701187753 1
< 0.1%
9.58086891 1
< 0.1%
9.449986995 1
< 0.1%
9.448175472 1
< 0.1%
9.447938944 1
< 0.1%
9.427385365 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.6 KiB
True
4378 
False
4315 
ValueCountFrequency (%)
True 4378
50.4%
False 4315
49.6%
2024-12-19T00:39:17.993629image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Deck
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)0.1%
Missing199
Missing (%)2.3%
Memory size68.0 KiB
F
2794 
G
2559 
E
876 
B
779 
C
747 
Other values (3)
739 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8494
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowF
3rd rowA
4th rowA
5th rowF

Common Values

ValueCountFrequency (%)
F 2794
32.1%
G 2559
29.4%
E 876
 
10.1%
B 779
 
9.0%
C 747
 
8.6%
D 478
 
5.5%
A 256
 
2.9%
T 5
 
0.1%
(Missing) 199
 
2.3%

Length

2024-12-19T00:39:18.078440image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T00:39:18.174688image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
f 2794
32.9%
g 2559
30.1%
e 876
 
10.3%
b 779
 
9.2%
c 747
 
8.8%
d 478
 
5.6%
a 256
 
3.0%
t 5
 
0.1%

Most occurring characters

ValueCountFrequency (%)
F 2794
32.9%
G 2559
30.1%
E 876
 
10.3%
B 779
 
9.2%
C 747
 
8.8%
D 478
 
5.6%
A 256
 
3.0%
T 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 8494
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 2794
32.9%
G 2559
30.1%
E 876
 
10.3%
B 779
 
9.2%
C 747
 
8.8%
D 478
 
5.6%
A 256
 
3.0%
T 5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 8494
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 2794
32.9%
G 2559
30.1%
E 876
 
10.3%
B 779
 
9.2%
C 747
 
8.8%
D 478
 
5.6%
A 256
 
3.0%
T 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8494
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 2794
32.9%
G 2559
30.1%
E 876
 
10.3%
B 779
 
9.2%
C 747
 
8.8%
D 478
 
5.6%
A 256
 
3.0%
T 5
 
0.1%

Cabin_No
Real number (ℝ)

MISSING 

Distinct1817
Distinct (%)21.4%
Missing199
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean600.36767
Minimum0
Maximum1894
Zeros18
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size68.0 KiB
2024-12-19T00:39:18.285258image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile31
Q1167.25
median427
Q3999
95-th percentile1569.35
Maximum1894
Range1894
Interquartile range (IQR)831.75

Descriptive statistics

Standard deviation511.86723
Coefficient of variation (CV)0.85258959
Kurtosis-0.71277235
Mean600.36767
Median Absolute Deviation (MAD)329
Skewness0.71835962
Sum5099523
Variance262008.06
MonotonicityNot monotonic
2024-12-19T00:39:18.400891image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82 28
 
0.3%
86 22
 
0.3%
19 22
 
0.3%
56 21
 
0.2%
176 21
 
0.2%
97 21
 
0.2%
230 20
 
0.2%
269 19
 
0.2%
65 19
 
0.2%
123 19
 
0.2%
Other values (1807) 8282
95.3%
(Missing) 199
 
2.3%
ValueCountFrequency (%)
0 18
0.2%
1 15
0.2%
2 11
0.1%
3 16
0.2%
4 7
 
0.1%
5 13
0.1%
6 12
0.1%
7 9
0.1%
8 13
0.1%
9 16
0.2%
ValueCountFrequency (%)
1894 1
< 0.1%
1893 1
< 0.1%
1892 1
< 0.1%
1891 1
< 0.1%
1888 2
< 0.1%
1886 1
< 0.1%
1884 1
< 0.1%
1880 1
< 0.1%
1878 1
< 0.1%
1877 1
< 0.1%

Cabin_Side
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing199
Missing (%)2.3%
Memory size68.0 KiB
S
4288 
P
4206 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8494
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowP
2nd rowS
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S 4288
49.3%
P 4206
48.4%
(Missing) 199
 
2.3%

Length

2024-12-19T00:39:18.509358image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T00:39:18.589491image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
s 4288
50.5%
p 4206
49.5%

Most occurring characters

ValueCountFrequency (%)
S 4288
50.5%
P 4206
49.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 8494
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 4288
50.5%
P 4206
49.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 8494
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 4288
50.5%
P 4206
49.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8494
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 4288
50.5%
P 4206
49.5%

ID_Group
Real number (ℝ)

Distinct6217
Distinct (%)71.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4633.3896
Minimum1
Maximum9280
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.0 KiB
2024-12-19T00:39:18.682518image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile465.6
Q12319
median4630
Q36883
95-th percentile8819.4
Maximum9280
Range9279
Interquartile range (IQR)4564

Descriptive statistics

Standard deviation2671.0289
Coefficient of variation (CV)0.57647404
Kurtosis-1.1817463
Mean4633.3896
Median Absolute Deviation (MAD)2277
Skewness0.0020202219
Sum40278056
Variance7134395.1
MonotonicityIncreasing
2024-12-19T00:39:18.794581image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4498 8
 
0.1%
8168 8
 
0.1%
8728 8
 
0.1%
8796 8
 
0.1%
8956 8
 
0.1%
4256 8
 
0.1%
984 8
 
0.1%
9081 8
 
0.1%
8988 8
 
0.1%
5756 8
 
0.1%
Other values (6207) 8613
99.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 1
 
< 0.1%
3 2
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 2
< 0.1%
7 1
 
< 0.1%
8 3
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
9280 2
< 0.1%
9279 1
 
< 0.1%
9278 1
 
< 0.1%
9276 1
 
< 0.1%
9275 3
< 0.1%
9274 1
 
< 0.1%
9272 2
< 0.1%
9270 1
 
< 0.1%
9268 1
 
< 0.1%
9267 2
< 0.1%

ID_No
Categorical

IMBALANCE 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size68.0 KiB
01
6217 
02
1412 
03
 
571
04
 
231
05
 
128
Other values (3)
 
134

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters17386
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row01
2nd row01
3rd row01
4th row02
5th row01

Common Values

ValueCountFrequency (%)
01 6217
71.5%
02 1412
 
16.2%
03 571
 
6.6%
04 231
 
2.7%
05 128
 
1.5%
06 75
 
0.9%
07 46
 
0.5%
08 13
 
0.1%

Length

2024-12-19T00:39:18.888103image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-19T00:39:18.979801image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
01 6217
71.5%
02 1412
 
16.2%
03 571
 
6.6%
04 231
 
2.7%
05 128
 
1.5%
06 75
 
0.9%
07 46
 
0.5%
08 13
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 8693
50.0%
1 6217
35.8%
2 1412
 
8.1%
3 571
 
3.3%
4 231
 
1.3%
5 128
 
0.7%
6 75
 
0.4%
7 46
 
0.3%
8 13
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17386
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8693
50.0%
1 6217
35.8%
2 1412
 
8.1%
3 571
 
3.3%
4 231
 
1.3%
5 128
 
0.7%
6 75
 
0.4%
7 46
 
0.3%
8 13
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 17386
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8693
50.0%
1 6217
35.8%
2 1412
 
8.1%
3 571
 
3.3%
4 231
 
1.3%
5 128
 
0.7%
6 75
 
0.4%
7 46
 
0.3%
8 13
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17386
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8693
50.0%
1 6217
35.8%
2 1412
 
8.1%
3 571
 
3.3%
4 231
 
1.3%
5 128
 
0.7%
6 75
 
0.4%
7 46
 
0.3%
8 13
 
0.1%

Interactions

2024-12-19T00:37:11.105325image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:33:10.588570image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:33:41.771772image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:34:11.879025image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:34:41.579606image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:35:11.919740image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:35:42.313268image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:36:14.165261image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:37:22.129630image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:33:10.668358image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:33:41.855277image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:34:11.958789image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:34:41.662730image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:35:12.003095image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:35:42.398612image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:36:17.241443image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:37:33.104430image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:33:10.744862image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:33:41.935314image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:34:12.045755image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:34:41.738919image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:35:12.079639image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:35:42.475150image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:36:20.389416image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:37:46.470824image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:33:10.822715image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:33:42.013875image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:34:12.120619image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:34:41.818616image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:35:12.159716image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:35:42.557237image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:36:23.522159image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:37:57.417947image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:33:10.900231image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:33:42.095189image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:34:12.193768image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:34:41.894649image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:35:12.236342image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:35:42.635724image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:36:26.543145image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:38:08.350981image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:33:10.977480image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:33:42.169348image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:34:12.267846image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:34:41.968873image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:35:12.311340image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:35:42.716528image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:36:29.661558image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:38:22.096961image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:33:11.059823image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:33:42.246819image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:34:12.345201image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:34:42.048942image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:35:12.394209image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:35:42.800098image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:36:34.317020image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:38:37.593587image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:33:16.122633image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:33:47.099703image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:34:17.124667image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:34:47.595045image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:35:17.267158image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:35:48.967650image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-19T00:36:42.218928image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2024-12-19T00:39:19.067429image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
AgeCabin_NoCabin_SideCryoSleepDeckDestinationFoodCourtHomePlanetID_GroupID_NoRoomServiceShoppingMallSpaTransportedVIPVRDeck
Age1.000-0.0050.0160.1170.1310.0400.2000.195-0.0050.1030.1210.0990.1910.1320.1140.177
Cabin_No-0.0051.0000.0000.0000.0000.145-0.0050.393-0.2450.000-0.002-0.0140.0010.1460.0000.008
Cabin_Side0.0160.0001.0000.0150.0370.0000.0080.037-0.0070.000-0.021-0.0160.0010.1030.000-0.011
CryoSleep0.1170.0000.0151.0000.3330.117-0.5360.114-0.0040.081-0.521-0.517-0.5520.4670.080-0.531
Deck0.1310.0000.0370.3331.0000.244-0.2740.743-0.0020.074-0.056-0.039-0.2270.2130.197-0.228
Destination0.0400.1450.0000.1170.2441.000-0.0230.256-0.0050.0530.0940.0920.0170.1090.043-0.011
FoodCourt0.200-0.0050.008-0.536-0.274-0.0231.0000.3260.0060.0370.1810.1850.4700.2890.1330.497
HomePlanet0.1950.3930.0370.1140.7430.2560.3261.000-0.0050.1350.1080.0450.0100.1910.171-0.062
ID_Group-0.005-0.245-0.007-0.004-0.002-0.0050.006-0.0051.0000.0000.0060.0130.0070.1790.0000.004
ID_No0.1030.0000.0000.0810.0740.0530.0370.1350.0001.000-0.103-0.114-0.0620.0890.021-0.060
RoomService0.121-0.002-0.021-0.521-0.0560.0940.1810.1080.006-0.1031.0000.4300.2400.3660.0550.181
ShoppingMall0.099-0.014-0.016-0.517-0.0390.0920.1850.0450.013-0.1140.4301.0000.2530.3060.0240.188
Spa0.1910.0010.001-0.552-0.2270.0170.4700.0100.007-0.0620.2400.2531.0000.3680.0860.437
Transported0.1320.1460.1030.4670.2130.1090.2890.1910.1790.0890.3660.3060.3681.0000.035-0.347
VIP0.1140.0000.0000.0800.1970.0430.1330.1710.0000.0210.0550.0240.0860.0351.0000.095
VRDeck0.1770.008-0.011-0.531-0.228-0.0110.497-0.0620.004-0.0600.1810.1880.437-0.3470.0951.000

Missing values

2024-12-19T00:39:15.124923image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-19T00:39:15.323328image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-19T00:39:15.451577image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

PassengerIdHomePlanetCryoSleepDestinationAgeVIPRoomServiceFoodCourtShoppingMallSpaVRDeckTransportedDeckCabin_NoCabin_SideID_GroupID_No
00001_01EuropaFalseTRAPPIST-1e39.0False-2.302585-2.302585-2.302585-2.302585-2.302585FalseB0P000101
10002_01EarthFalseTRAPPIST-1e24.0False4.6913482.1972253.2188766.3080983.784190TrueF0S000201
20003_01EuropaFalseTRAPPIST-1e58.0True3.7612008.182000-2.3025858.8120993.891820FalseA0S000301
30003_02EuropaFalseTRAPPIST-1e33.0False-2.3025857.1569565.9162028.1104275.262690FalseA0S000302
40004_01EarthFalseTRAPPIST-1e16.0False5.7137334.2484955.0172806.3368260.693147TrueF1S000401
50005_01EarthFalsePSO J318.5-2244.0False-2.3025856.180017-2.3025855.673323-2.302585TrueF0P000501
60006_01EarthFalseTRAPPIST-1e26.0False3.7376707.3388881.098612-2.302585-2.302585TrueF2S000601
70006_02EarthTrueTRAPPIST-1e28.0False-2.302585-2.302585-2.302585-2.302585-2.302585TrueG0S000602
80007_01EarthFalseTRAPPIST-1e35.0False-2.3025856.6656842.8332135.375278-2.302585TrueF3S000701
90008_01EuropaTrue55 Cancri e14.0False-2.302585-2.302585-2.302585-2.302585-2.302585TrueB1P000801
PassengerIdHomePlanetCryoSleepDestinationAgeVIPRoomServiceFoodCourtShoppingMallSpaVRDeckTransportedDeckCabin_NoCabin_SideID_GroupID_No
86839272_02EarthFalseTRAPPIST-1e21.0False4.4543471.0986125.0039465.3375385.796058FalseF1894P927202
86849274_01EarthTrueTRAPPIST-1e23.0False-2.302585-2.302585-2.302585-2.302585-2.302585TrueG1508P927401
86859275_01EuropaFalseTRAPPIST-1e0.0False-2.302585-2.302585-2.302585-2.302585-2.302585TrueA97P927501
86869275_02EuropaFalseTRAPPIST-1e32.0False0.0000007.044033-2.3025853.9120233.526361FalseA97P927502
86879275_03EuropaFalseTRAPPIST-1e30.0False-2.3025858.073403-2.3025850.6931475.799093TrueA97P927503
86889276_01EuropaFalse55 Cancri e41.0True-2.3025858.827468-2.3025857.4042794.304065FalseA98P927601
86899278_01EarthTruePSO J318.5-2218.0False-2.302585-2.302585-2.302585-2.302585-2.302585FalseG1499S927801
86909279_01EarthFalseTRAPPIST-1e26.0False-2.302585-2.3025857.5347630.000000-2.302585TrueG1500S927901
86919280_01EuropaFalse55 Cancri e32.0False-2.3025856.955593-2.3025855.8664688.081784FalseE608S928001
86929280_02EuropaFalseTRAPPIST-1e44.0False4.8362828.452761-2.302585-2.3025852.484907TrueE608S928002